Data processing comprises activities such as data entry, data storage, data analysis (such as searching, aggregating, sorting, and otherwise manipulating the data), and data display (such as media outputs, printouts, screen displays, tables, charts, and graphs). With small data sets these activities can be performed in a relatively straightforward manner on a single device with a memory capacity capable of handling the data set. However, with a large data set, for example one whose size exceeds the memory capacity of any one device (for example all the websites of the world wide web), components of the data set must be broken up between multiple independently operating devices. Coordinating the performance of these independently operating devices for data processing involves additional complexities and complications. A distributed data environment is an architecture for data processing which attempts to address these complexities and complications.
The complexities and complications inherent to a distributed data environment are myriad. For example, a system must be in place for cataloging and locating each of the data sets but this system must itself be complex enough to address differences in where the components are stored and how they can be accessed. Moreover, because the numerous independent devices are collectively dynamic, meaning they are often failing, being moved, taken offline, being replaced, being upgraded, etc., the system for cataloging and locating needs to have a built-in flexibility to react to this dynamism. Also, because the data is so vast, standard data processing techniques relying on moving targeted data sets back and forth between applications are not feasible. This is because they would cause data traffic obstructions and would make maintaining consistencies in the data set among concurrent data processing of the data set impossible.
Many prior art attempts have been made to address these complexities. A widespread problem innate to these attempts is that they do not provide specifically crucial information at a specifically appropriate time or specifically appropriate manner to practically address such complexities. This is because diagnostic processes for a distributed data environment are themselves vulnerable to the same inherent complexities as the distributed data environment itself. As a result, because system failures and errors can occur rapidly but diagnoses of system failures and errors can only occur more slowly, there is clear utility in and benefit from, novel methods and apparatuses for accurately and efficiently implementing diagnoses of and improvements in the performance of a distributed data environment.
For the purposes of this disclosure, like reference numerals in the figures shall refer to like features unless otherwise indicated. The drawings are only an exemplification of the principles of the invention and are not intended to limit the disclosure to the particular embodiments illustrated.